Did the Federal Reserves' Quantitative Easing (QE) in the aftermath of the financial crisis have macroeconomic effects? To answer this question, we estimate a large-scale DSGE model over the sample from 1998 until 2020, including data of the Fed's balance sheet. We allow for QE to affect the...

Did the Federal Reserve's Quantitative Easing (QE) in the aftermath of the financial crisis have macroeconomic effects? To answer this question, we estimate a large-scale DSGE model over the sample from 1998 until 2020, including data of the Fed's balance sheet. We allow for QE to affect the...

Using a nonlinear Bayesian likelihood approach that fully accounts for the lower bound on nominal interest rates, we analyze US post-crisis macroeconomic dynamics and provide reference parameter estimates. We find that despite the attention received in the literature, neither the inclusion of...

Did the Federal Reserves' Quantitative Easing (QE) in the aftermath of the financial crisis have macroeconomic effects? To answer this question, the authors estimate a large-scale DSGE model over the sample from 1998 to 2020, including data of the Fed's balance sheet. The authors allow for QE to...

Using a nonlinear Bayesian likelihood approach that fully accounts for the zero lower bound on nominal interest rates, the authors analyze US post-crisis business cycle dynamics and provide reference parameter estimates. They find that neither the inclusion of financial frictions nor that of...

We propose a set of tools for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models while accounting for the effective lower bound on nominal interest rates. We combine a novel nonlinear recursive filter with a computationally efficient piece-wise linear solution...

We use nonlinear Bayesian methods to evaluate the performance of financial frictions á la Bernanke et al. (1999) during and after the Global Financial Crisis. We find that, despite the attention received in the literature, including these frictions in the canonical medium-scale DSGE model does...

We propose a new approach for the efficient and robust Bayesian estimation of medium- and large-scale DSGE models with occasionally binding constraints. At its core lies the Ensemble Kalman filter, a novel nonlinear recursive filter, which allows for fast likelihood approximations even for...